Abstract

Abstract
In the real world, the human eye is confronted with a wide range of luminances from bright sunshine to low night light. Our eyes cope with this vast range of intensities by adaptation; changing their sensitivity to be responsive at different illumination levels. This adaptation is highly localized, allowing us to see both dark and bright regions of a high dynamic range environment. In this paper we present a new model of eye adaptation based on physiological data. The model, which can be easily integrated into existing renderers, can function either as a static local tone mapping operator for single high dynamic range image, or as a temporal adaptation model taking into account time elapsed and intensity of preadaptation for a dynamic sequence. We finally validate our technique with a high dynamic range display and a psychophysical study. Copyright © 2004 by the Association for Computing Machinery, Inc.

Abstract
Complex applications in distributed-memory parallel systems often follow a demand-driven approach with domain decomposition. A uniform data distribution among the local memories at the processing elements may require frequent remote data access. To keep the processors busy while data is remotely fetched, concurrent application processes are assigned to each transputer-based processing element. Adding more concurrent application processes in a large-scale parallel system may degrade performance, due to the traffic increase with data requests and data block replies. A conditional broadcast mechanism is implemented during data requests, to limit this flow of messages. Monitoring strategies are proposed to further reduce the messages density, and a parameterized model to measure and evaluate global execution times is presented. Simulation data running the model with up to 35 transputers show that monitoring can reduce the performance degradation when more local concurrence is added. However, if too much data replication is present, the simulation data also show that the supply of communication services at each node still imposes a burden, requiring complementary monitoring strategies to allow removal of redundant reply messages.

Abstract
Many of the current approaches used in teaching and learning in engineering education are not the most appropriate to prepare students for the challenges they will face in their professional careers. The active involvement of students in their learning process facilitates the development of the technical and professional competencies they need as professionals. This article describes the organisation and impact of a mini-conference and project work - the creation of a software product and its introduction in the market - aimed at the development of professional competencies in general and writing skills in particular. The course was evaluated by assessing the students' perception of the development of a number of professional competencies through a questionnaire completed by 125 students from two consecutive year groups. The results indicate that the project work and the mini-conference had a positive impact on students' perceptions of the development of professional competencies. © 2012 Copyright SEFI.

Abstract
Load distribution is a well known critical problem in every distributed system. From operating systems to agent oriented applications it is not difficult to find cases where processing nodes are overloaded when, at the same time, other peers present low levels of activity. In agent oriented applications, where the appeal to cooperation is almost a constant event, these unbalanced situations may generate serious cases of contention, deadlock or simply large idle times. The implementation of load distribution strategies in a distributed system may help significantly to improve its overall performance and reduce effectively such undesirable situations. In order to study the effects of different load distribution policies in agent based applications a generic load distribution simulation system was design and implemented. The system allows the specification of multiorganisational distributed systems with dynamic load patterns. Its main characteristics and functionalities are presented in this paper.

Abstract
This article delves into the role of the quantum Fisher information matrix (FIM) in enhancing the performance of parameterized quantum circuit (PQC)-based reinforcement learning agents. While previous studies have highlighted the effectiveness of PQC-based policies preconditioned with the quantum FIM in contextual bandits, its impact in broader reinforcement learning contexts, such as Markov decision processes, is less clear. Through a detailed analysis of L & ouml;wner inequalities between quantum and classical FIMs, this study uncovers the nuanced distinctions and implications of using each type of FIM. Our results indicate that a PQC-based agent using the quantum FIM without additional insights typically incurs a larger approximation error and does not guarantee improved performance compared to the classical FIM. Empirical evaluations in classic control benchmarks suggest even though quantum FIM preconditioning outperforms standard gradient ascent, in general, it is not superior to classical FIM preconditioning.